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yolov4.yaml
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yolov4.yaml
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# parameters
number_classes: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# CSPDarknet53-SPP backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, BottleneckCSP, [64]],
[-1, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [128, 3, 2]], # 4-P2/4
[-1, 2, BottleneckCSP, [128]],
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [256, 3, 2]], # 7-P3/8
[-1, 8, BottleneckCSP, [256]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [512, 3, 2]], # 10-P4/16
[-1, 8, BottleneckCSP, [512]],
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [1024, 3, 2]], # 13-P5/32
[-1, 4, BottleneckCSP, [1024]],
[-1, 1, Conv, [1024, 1, 1]], # 15
]
# YOLOv5 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [1024, 3, 1]],
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [1024, 3, 1]],
[-1, 1, Conv, [512, 1, 1]], # 22
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
[[-1, 12], 1, Concat, [1]], # concat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 26
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
[[-1, 9], 1, Concat, [1]], # concat backbone P3
[-1, 3, BottleneckCSP, [256, False]], # 30
[-1, 1, Conv, [256, 3, 2]],
[[-1, 27], 1, Concat, [1]], # concat head P4
[-1, 3, BottleneckCSP, [512, False]], # 33
[-1, 1, Conv, [512, 3, 2]],
[[-1, 23], 1, Concat, [1]], # concat head P5
[-1, 3, BottleneckCSP, [1024, False]], # 36
[[30, 33, 36], 1, Detect, [number_classes, anchors]], # Detect(P3, P4, P5)
]